Manufacturing is one of the top 5 big data markets, in which the market share of manufacturing is about 10%. According to one reach, the failure of the important equipment is the main factor that influences the operation of the company. If one can analyze the failure of the important equipment, the operation efficiency can be improved and the competitiveness of enterprises can be greatly increased.
Prognostics and health management can be used to monitor and evaluate the state of the equipment (or its component). The time to maintain or replace a component can be decided by the state of the equipment (or its component) in order to reduce unscheduled downtime. More specifically, if the remaining useful life of the component can be accurately predicted, the component can be maintained or replaced in advance. However, the amount of data of some of the components may not be enough and the variability of the failure of the components is high, such that the predicting model is difficult to be obtained. Therefore, accurately predicting the remaining useful life according to little data is an important issue nowadays.